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An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT...
Generating accurate and robust classification maps from hyperspectral imagery (HSI) depends on the choice of the classifiers and input data sources. Choosing the appropriate classifier for a problem at hand is a tedious task. Multiple classifier system (MCS) combines the relative merits of various classifiers to generate robust classification maps. However, the presence of inaccurate classifiers may...
Class label noise is a data-level difficulty associated with training objects with incorrectly assigned labels. This problem may originate from poorly documented historic data, errors during data generation process or mistakes made by human experts. Inclusion of such examples during the training process will mislead the classifier by presenting a falsified class distribution and consequently lead...
Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are often categorised into six groups (homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells), which...
The performance of hyperspectral classification is affected by within-class spectral variation since different materials may present similar spectral signatures. In this paper, we investigate how to fully use the low-rank property of hyperspectral images to alleviate spectra variation. Particulary, two effective strategies that explore the low-rank property in local spectral and spatial space are...
Advancements in Sonar image capture have opened the door to powerful classification schemes for automatic target recognition (ATR). Recent work has particularly seen the application of sparse reconstruction-based classification (SRC) to sonar ATR, which provides compelling accuracy rates even in the presence of noise and blur. However, existing sparsity based sonar ATR techniques assume that the test...
In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts:...
Face recognition provides a challenging issue in the domain of analyzing images. In this paper a novel approach for face recognition using hybrid SIFT-SVM is proposed. The current database is divided into two parts; training and testing database. The SIFT feature will be created for each training images and the keypoints are computed; then the SVM is applied for the matching process for test images...
Although electronic system has entered the digital age, analog circuit is still an essential part. Therefore the performance monitoring or evaluation of analog circuit is extremely important. However some problems about analog circuit performance monitoring is being, such as data acquisition online of the industry field with uncertainty, performance monitoring timeliness. Here an online performance...
When collecting samples via crowd-sourcing for semi-supervised learning, often labels that designate events of interest are assigned unreliably, resulting in label noise. In this paper, we propose a robust method for graph-based image classifier learning given noisy labels, leveraging on recent advances in graph signal processing. In particular, we formulate a graph-signal restoration problem, where...
The problem of online face tracking from unconstrained videos is still unresolved. Challenges range from coping with severe online appearance variations to coping with occlusion. We propose RFTD (Robust Face Tracking-by-Detection), a system which combines tracking and detection into a single framework to robustly track a face from unconstrained videos. RFTD is based on the idea that adaptive and stable...
Real-time human detection in crowded and dynamic environments poses a significant challenge, due to complex background, occlusion and different human poses. In this paper, we propose a two-staged approach using color and depth data taken by an RGB-D camera. The first stage is to find plausible head-top locations quickly in depth image. The second stage is to extract effective discrimination features...
An object often has many distinct manifestations in computer vision, which brings a great challenge to utilizing more comprehensive information. Inspired by some biological researches about edge sensitivity and global structure priority, our key insight is to establish unified transfer classification network with shared contour information. Combining two convolutional networks with three cascaded...
Considering the health monitoring requirement of Electronic System, two indexes, such as detection speed and detection reliability, are indispensable. Here a data driven dynamic health monitoring (DDDHM) method is presented. The main idea of DDDHM is to employ a robust learning machine robust least square support vector regression (LSSVR) to monitor quality of electronic system. As to obtain more...
In this article considered the ways of robust solutions construction based on the method of pseudo-observations and weighted method LS-SVM using Huber's simple and adapted loss function.
Emerging from their initial application to functional imaging data, multivoxel pattern classifiers are increasingly applied to structural data to predict, among many others, disease status and psychological traits. Naturally, the choice of a suitable classifier in such analyses is a critical step. In the present paper, we assessed the performance of two frequently used classifiers (Support Vector...
This paper aims at comparing two local outliers detection techniques. One is based on a Least Squares Support Vector Machine technique within a sliding window-based learning algorithm. A modification is proposed to improve its performance in non-stationary time-series. The second method relies on the Principal Component Analysis theory along with a robust orthonormal projection approximation subspace...
The shares are considered as a fundamental part of the equity market, as their values change over time as a result of offer and demand, and the effect of market volatility. This volatility makes the trading of shares on a stock exchange is an extremely difficult task. That is why in this article develops and analyzes a system for automatic trading shares, which incorporates a series of progressive...
Imbalanced data is an inevitable problem in many real world problems, including bleeding detection from endoscopic videos with a fewer clinically significant examples outnumbered by normal examples. In this paper, we have presented a comprehensive analysis of six different classifier performance for different class distribution of training dataset. We have addressed two questions: 1. Is there any...
Watermarking has been used widely for passing the confidential information over Internet, a good watermark will be imperceptible, robust over the various geometric and non-geometric attacks. The watermark shouldn't be easily extracted by the hacker, to achieve this several watermarking methodologies were developed for different domains namely spatial, frequency and wavelet domains using DCT, DWT and...
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